The present invention relates to video analytics for video monitoring system applications, and more particularly relates to a new and novel video analytics function and module that enables an end-user of a video monitoring system comprising the novel function to identify objects in an imaging field of view (FOV) acquired as steaming video from a video source, define a border around the object as an object field definition, and the further define an amount of compression for the data comprising the identified object field. As such, each subsequent frame of the captured FOV (the streaming video) and object field are forwarded from the video source, where the data comprising the user-defined object field definition is reduced compared to the other FOV video data commensurate with the user-defined compression level for the object field. The inventive operation minimizes the bandwidth required to transfer the streaming video, and for processing the streaming video for surveillance purposes, and preferably nevertheless monitors the compressed object field in the FOV by use of a motion vector derived from the object field.
Video surveillance systems are known for use in a variety of applications for monitoring objects within an environment, e.g., a piece of baggage or a casino employee. Analog closed circuit television (CCTV) monitoring systems have been in operation for many years. These legacy analog-based CCTV systems, and more recently available network-based video surveillance systems are employed to monitor and/or track individuals and vehicles entering or leaving a building facility or security gate (entry/exit), individuals present within, entering/exiting a store, casino, office building, hospital, etc., or any other known setting where the health and/or safety of the occupants may be of concern. Video surveillance has long been employed in the aviation industry to monitor the presence of individuals at key locations within an airport, such as at security gates, baggage area, parking garages, etc.
CCTV-acquired image data has traditionally been recorded to videocassette recorders (VCRs) in CCTV, or hybrid CCTV/digital network-based surveillance systems. But the recent improvements in digital technology and digital network technology have lead to marked improvements in digital network-based surveillance systems. Such improvements include storing digital or digitized video data via digital video recorders (DVRs), or network video recorders (NVRs). CCTV cameras, however, because of their analog construction and operation, are notoriously difficult to integrate with conventional networks and systems. That is, many legacy CCTV-based video surveillance systems are modified to operate in the digital world, wherein the CCTV cameras' on-board processes must digitize the video data streams to operate as part of the digital network, or the Internet.
The phrases “network camera,” “video camera” or “video source” are used interchangeably herein to denote and describe video capture or video acquisition devices that may take the form of any of digital cameras, digital video recorders, analog CCTV cameras, etc., streamers, including video devices that include on-board servers and/or on-board video analytics known in the art for capturing a monitoring field of view (FOV) for a monitoring application. Digital network cameras perform many of the same functions performed by conventional analog CCTV cameras, but with greater functionality and reduced costs. Network cameras are typically interfaced directly into an Ethernet-based network at an Ethernet port through a video server (as mentioned above), a monitor either a fixed or moving FOV. Network camera video outputs may be viewed in their simplest form using a web browser at a PC (and PC monitor). Alternatively, the video feed from a network camera may be processed to accommodate more complex security-related solutions using dedicated software and application programs.
Video servers, or servers that provide video analytics functionality, may be included in a video surveillance system or network in order to process video provided by the network cameras. Video servers may be used in video management systems to operate upon analog CCTV video data, such operations including digitization, rasterization and processing by video analytics. Such video servers thereafter direct the video data to in-network or IP address locations, such as a video client. A single video server may network up to four analog cameras, converting the analog stream to frames of digital image data. Network or IP Cameras with on-board video analytics processing abilities shall be referred to herein as “smart IP cameras,” or smart video sources. Smart IP cameras or video sources allow for video analytics to be performed at the source of video data acquisition, that is, at the camera.
Video sequences, or streaming video acquired by network cameras of monitoring FOVs, both digital and analog, comprise frames of images of an FOV, and are streamed over the network using TCP/IP protocols and the like. The video streams are directed to distant servers (for example, by the streams' intended MAC address), or other video clients where the video surveillance data are analyzed by the server or video client applications using various known video analytics. Alternatively, the streaming video may be stored in a video database, and later accessed by video clients. Video analytics as used herein shall refer to functional operations performed by a video source to acquire video surveillance data, and performed on acquired video data by software or application programs that employ algorithms to detect classify, analyze objects in a field of view (FOV), and respond to such detection, classification and analyzing.
Video analytics are used in various conventional video-monitoring systems to enhance the effectiveness of the video monitoring for event and object detection, and reporting. Video analytics include various functions that provide for improved monitoring vigilance, improved video acquisition device functionality, monitoring functionality and analysis, and automated video monitoring system responsiveness. Known video analytics provide object-tracking features by which an object under surveillance is tracked or monitored by a camera. For example, video analytics may support video monitoring by analyzing streaming video surveillance data including an object under surveillance to detect if the object's position changes, e.g., the object has been removed from the location. If the object is moved, an alarm will generally be raised.
Various entities are known that provide video monitoring systems and software applications for video monitoring applications that include video analytics functioning. For example, IOImage, Inc., provides video analytics solutions marketed Intelligent Video Appliances,™ which performs various security-monitoring functions upon acquired video surveillance data. The Several Intelligent Video Appliances™ functions include without limitation intrusion detection by video surveillance, unattended baggage detection, stopped vehicle detection, and other video analytics functions such as autonomous person/vehicle tracking with pan/tilt/zoom (PZT).
Known video surveillance systems and video analytics techniques provide for monitoring high-risk environments such as airports, as mentioned above, but may be used as well in “home” video surveillance monitoring, traffic flow monitoring to monitor driver action at fixed street or highway locations, etc. Highway video surveillance operation is discussed at length in a paper by Li, et al., A HIDDEN MARKOV MODEL FRAMEWORK FOR TRAFFIC EVENT DETECTION USING VIDEO FEATURES; Mitsubishi Research Laboratories, (TR-2004-084; October 2004). Therein, a video analytics approach to highway traffic detection is described in which the video analytics extract features directly from compressed video to detect traffic events using a Gaussian hidden Markhov model (HMM) framework. The approach uses MPEG compression to reduce spatial redundancy between successive frames, the result of which is stored in a motion vector (MV) in video. MVs may describe an object found in acquired video frames in the spatial domain, where the magnitude of MV reflects the speed of the moving object and its direction indicates the moving direction of the moving object.
Another known video surveillance system and application is disclosed in US Patent Application No. 2006/0239645 (“the '645 application”), filed Mar. 31, 2005, commonly owned and incorporated by reference. The '645 application discloses an enterprise video surveillance system that includes video analytics abilities, including the ability to package video sequences derived from network cameras based on user-specified events. A video analytics processing manager, or Digital Video Manager™ (“DVM”), provides for portions of acquired video, e.g., acquired video sequences, to be bound into a “package” containing an event of interest captured by a digital video system sensor or video camera. DVM is a scalable, enterprise class IP-based digital video manager system that includes a network video recorder (NVR) capable of transforming standard IT equipment and component video sources into customized and manageable video systems for security and surveillance needs.
The packaged video sequences or events are transmitted by the DVM to an external agent (video client) for further analysis, for example, to a central monitoring location within the enterprise video surveillance system. One example of a video event that may be packaged by the DVM system includes a video clip containing facial images of an individual under surveillance, or enter/exiting a secured location. By packaging the video event in order that the segment is easily accessed, prompt security agent action may be taken with respect to the monitored individual's actions, etc. To that end, Honeywell's DVM systems include video analytics ability to provide relevant acquired or captured video data on demand, implement outdoors motion detection, conduct object tracking, conduct object tracking with classification, etc.
What would be welcomed in the field of enterprise-wide video surveillance, and video analytics-based video surveillance systems is a video analytics function that enables an end-user to identify objects in an imaging field of view (FOV) acquired by a video source that are not a priority, and limit the video data comprising the object in the frame of FOV, to minimize the amount of data transferred and processed. For that matter, it would be desirable to have a user option to define an amount of compression for the data comprising the identified object. Whereafter, each subsequent frame of the FOV would have a size equal to the data comprising the FOV set off by the compression ratio of the object data (in the streaming video). Such operation realizes a desirable effect of minimizing the bandwidth required to transfer the streaming video, and for processing the streaming video for surveillance purposes.